How Conversational AI Works
Conversational AI processes large volumes of data that enable the software solutions to recognize speech and simulate interpersonal communication. A feedback loop is created that relies on a combination of natural language processing (NLP) and machine learning (ML). A four-step process is used that includes input generation, input analysis, dialogue management, and reinforcement learning.
Over time, ML processes refine the AI algorithms to attain higher levels of understanding so a chatbot can derive actionable meaning from a conversation. By processing more interactions and larger amounts of data, a conversation AI entity can improve its ability to recognize patterns and produce more accurate responses to human queries.
Conversational AI in the Cloud
Conversational AI requires a tremendous amount of storage space and computing power as well as access to cutting-edge AI and ML technology. While these resources are not available to many organizations in-house, they are abundant in the public cloud. Following are some of the conversational AI offerings of cloud providers.
Azure’s Bot Service provides a development environment designed for building enterprise-grade conversational AI solutions. Users can customize applications to focus on customer requirements and extend their brand. The Bot Framework Composer offers a template-based bot-building platform that accelerates the development process.
Google has multiple categories of conversational AI solutions to address specific business usage scenarios. The offerings aim to improve customer acquisition, enhance the customer experience, and reduce service costs. Businesses can deploy conversational AI that focuses on customer care, search engine efficiency, IoT and custom hardware solutions, and providing voice assistance to users.
This conversational AI offering is designed to improve customer interactions and provide them with a more personalized experience. The bots perform textual and sentiment analysis in real-time to identify how a conversation should be routed and anticipate a customer’s needs.
The Digital Assistant uses natural language understanding (NLU) and custom AI algorithms to accurately understand common conversations. The tool enables developers to create conversational experiences in business applications and with customers through text, chat, and voice interfaces. Pre-built skills and templates enable customers to get started quickly with this secure and scaleable conversational AI solution.
Companies can create personalized experiences and obtain a deeper understanding of their customers through the use of the LivePerson Conversational Cloud platform. Increasing customer satisfaction with LivePerson helps drive revenue and increase brand awareness with this easily managed and automated conversational AI solution.
Public cloud offerings enable businesses of any size to take advantage of this innovative and practical use of AI and ML technology. As conversational AI becomes more entrenched in everyday consumer interactions, companies that fail to realize its potential may be reduced to conversing with a vastly reduced pool of customers.